---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-full_text_wt_val_1008
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# donut-base-full_text_wt_val_1008

This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1060

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9227        | 0.2   | 100  | 0.6985          |
| 0.6852        | 0.4   | 200  | 0.4421          |
| 0.5102        | 0.6   | 300  | 0.3346          |
| 0.4178        | 0.79  | 400  | 0.2886          |
| 0.4476        | 0.99  | 500  | 0.2455          |
| 0.2931        | 1.19  | 600  | 0.2287          |
| 0.2647        | 1.39  | 700  | 0.2072          |
| 0.2418        | 1.59  | 800  | 0.1905          |
| 0.3031        | 1.79  | 900  | 0.1754          |
| 0.2306        | 1.98  | 1000 | 0.1667          |
| 0.2031        | 2.18  | 1100 | 0.1619          |
| 0.1918        | 2.38  | 1200 | 0.1536          |
| 0.1802        | 2.58  | 1300 | 0.1504          |
| 0.1646        | 2.78  | 1400 | 0.1436          |
| 0.1816        | 2.98  | 1500 | 0.1379          |
| 0.1344        | 3.17  | 1600 | 0.1395          |
| 0.1752        | 3.37  | 1700 | 0.1336          |
| 0.1388        | 3.57  | 1800 | 0.1306          |
| 0.1402        | 3.77  | 1900 | 0.1262          |
| 0.1123        | 3.97  | 2000 | 0.1277          |
| 0.144         | 4.17  | 2100 | 0.1248          |
| 0.1077        | 4.37  | 2200 | 0.1226          |
| 0.1134        | 4.56  | 2300 | 0.1186          |
| 0.1192        | 4.76  | 2400 | 0.1179          |
| 0.1142        | 4.96  | 2500 | 0.1194          |
| 0.1426        | 5.16  | 2600 | 0.1202          |
| 0.1022        | 5.36  | 2700 | 0.1165          |
| 0.0815        | 5.56  | 2800 | 0.1164          |
| 0.1096        | 5.75  | 2900 | 0.1166          |
| 0.0866        | 5.95  | 3000 | 0.1121          |
| 0.1148        | 6.15  | 3100 | 0.1122          |
| 0.0771        | 6.35  | 3200 | 0.1129          |
| 0.0996        | 6.55  | 3300 | 0.1096          |
| 0.0622        | 6.75  | 3400 | 0.1099          |
| 0.0985        | 6.94  | 3500 | 0.1092          |
| 0.0684        | 7.14  | 3600 | 0.1097          |
| 0.0669        | 7.34  | 3700 | 0.1086          |
| 0.0624        | 7.54  | 3800 | 0.1088          |
| 0.0763        | 7.74  | 3900 | 0.1069          |
| 0.0579        | 7.94  | 4000 | 0.1060          |
| 0.0623        | 8.13  | 4100 | 0.1083          |
| 0.0599        | 8.33  | 4200 | 0.1058          |
| 0.0625        | 8.53  | 4300 | 0.1073          |
| 0.0499        | 8.73  | 4400 | 0.1059          |
| 0.0628        | 8.93  | 4500 | 0.1059          |
| 0.0684        | 9.13  | 4600 | 0.1063          |
| 0.0472        | 9.33  | 4700 | 0.1056          |
| 0.068         | 9.52  | 4800 | 0.1057          |
| 0.06          | 9.72  | 4900 | 0.1062          |
| 0.0636        | 9.92  | 5000 | 0.1060          |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1